Computational photography is a technique to create images using a computer and a variety of data processing methods, which are applied to a number of digital images taken from different view points. The resulting photographic images are, in some cases, the same as those that would have been created by a single camera with characteristics that would be too difficult or expensive to manufacture. For example, it is possible to generate images or a video like those captured from cameras with large lenses or unusual optics. It is also possible to create synthetic or virtual images as if they were taken from an inaccessible position. Image-based rendering is as related technique used to create photorealistic images of a real scene that is also based on a set of two-dimensional images of the scene captured from different view points, but in this ease, the set of two-dimensional images is used to compose a three-dimensional model that can be used to render images of novel views of the scene not captured in the original set of two-dimensional images.
In both techniques, the generation of virtual images is based on the concept of light-fields, which model the distribution of light intensity and propagation in a scene. A fundamental component of these imaging methods is the acquisition of information about the light field using several views. For example, light-field measurements can be performed by moving a single camera to capture images of a static scene from different viewing positions. However, in general, the quality of the synthetic views depends on the number and resolution of those views, so it can be much more convenient to capture the light field using arrays with a large number of cameras.
In recent years, with the advent of inexpensive digital image sensors and advances in semiconductor device miniaturization larger amounts of computing power is available for processing images at a lower cost. These new innovations were expected reduce the cost of rendering images captured using camera arrays. However, the cost of image rendering using images obtained from camera arrays has actually increased. The problem is not due to the price of individual components, integration, or assembly, but is instead due to the high cost of acquiring and simultaneously transmitting the large amounts of image and video data captured by the cameras to a single computing device for processing. For example, an array of 25×25 Mpixel cameras (high-definition video), each camera operated at 30 frames/second, generates 112 Gbytes/second, with nearly 4 Gbytes for each frame. An increasing demand for higher camera resolution, video quality, and a broad range of virtual camera views have also contributed to the increased cost of rendering images acquired using camera arrays.
Developers and users of camera arrays continue to seek systems that lower the cost of image rendering.